U.S. patent application number 13/214155 was filed with the patent office on 2012-02-23 for targeted publishing system based on profile mashups.
Invention is credited to Thomas C. Moran, Palle M. Pedersen.
Application Number | 20120047012 13/214155 |
Document ID | / |
Family ID | 45594810 |
Filed Date | 2012-02-23 |
United States Patent
Application |
20120047012 |
Kind Code |
A1 |
Pedersen; Palle M. ; et
al. |
February 23, 2012 |
Targeted Publishing System Based on Profile Mashups
Abstract
A targeted publishing system based on profile mashups improves
on traditional methods for identifying and delivering targeted
content to specific end users by using data from sources related to
one or more addresses (including physical addresses) for the end
user in order to understand more clearly what targeted content to
deliver to such specific end user.
Inventors: |
Pedersen; Palle M.;
(Brookline, MA) ; Moran; Thomas C.; (Dennis,
MA) |
Family ID: |
45594810 |
Appl. No.: |
13/214155 |
Filed: |
August 19, 2011 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
61375799 |
Aug 21, 2010 |
|
|
|
Current U.S.
Class: |
705/14.49 ;
707/756; 707/E17.009 |
Current CPC
Class: |
G06Q 30/02 20130101;
G06Q 30/0251 20130101; G06F 16/9535 20190101 |
Class at
Publication: |
705/14.49 ;
707/756; 707/E17.009 |
International
Class: |
G06F 17/30 20060101
G06F017/30; G06Q 30/00 20060101 G06Q030/00 |
Claims
1. A system for publishing targeted content comprising: a general
content management subsystem for managing general content in a
general content database; a targeted content management subsystem
for managing targeted content in a targeted content database; an
end user profile mashup subsystem for compiling profile mashups of
identified end users by combining end user information from an end
user profile database with data from other end user data sources
based on one or more end user addresses; a content targeting
subsystem for creating assembled content to deliver to identified
end users from the general content database and the targeted
content database using information from profile mashups compiled by
the end user profile mashup subsystem to select the targeted
content.
2. The system of claim 1, wherein the assembled content is
delivered to an end user device via a computer network.
3. The system of claim 2, wherein the assembled content is
delivered as a document.
4. The system of claim 2, wherein the assembled content is
delivered through an app.
5. The system of claim 2, wherein the assembled content is
delivered through a web-site.
6. The system of claim 1, wherein the assembled content is
delivered to an end user in a physical form.
7. The system of claim 1, wherein the end user address comprises a
physical address.
8. The system of claim 7, wherein the end user address comprises a
billing address.
9. The system of claim 7, wherein the end user address comprises a
shipping address.
10. The system of claim 1, wherein the identified end users are
identified using electronic identifiers.
11. The system of claim 10, wherein the identified end users are
identified using subscriber IDs.
12. The system of claim 10, wherein the identified end users are
identified using email addresses.
13. The system of claim 10, wherein the identified end users are
identified using website logins.
14. The system of claim 1, wherein the targeted content comprises
an advertisement.
15. The system of claim 1, further comprising: a targeted content
placement subsystem for managing targeted content placements, which
identify locations within the general content where targeted
content is to be placed, in a targeted content placement database,
and making the targeted content placements available to the content
targeting subsystem for use when assembling content.
16. The system of claim 1, wherein the assembled content comprises
a publication.
17. The system of claim 16, wherein the assembled content comprises
an electronic publication.
18. The system of claim 16, wherein the assembled content comprises
a printed publication.
19. The system of claim 1, wherein the end user profile mashup
subsystem compiles the profile mashups of identified end users by
combining end user information from an end user profile database
with data from other end user data sources where one or more of the
end user addresses approximately equals an address in the other end
user data sources.
20. A system for publishing targeted content comprising: a targeted
content management subsystem for managing targeted content in a
targeted content database; an end user profile mashup subsystem for
compiling profile mashups of identified end users by combining end
user information from an end user profile database with data from
other end user data sources based on one or more end user
addresses; and a content targeting subsystem for creating assembled
content to deliver to identified end users from the targeted
content database using information from profile mashups compiled by
the end user profile mashup subsystem to select the targeted
content.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This utility patent application claims priority from U.S.
provisional patent application Ser. No. 61/375,799, filed
21-Aug.-2010, titled "TARGETED PUBLISHING SYSTEM BASED ON PROFILE
MASHUPS" in the name of Palle M. Pedersen and Thomas C. Moran,
which is hereby fully incorporated by reference.
COPYRIGHT NOTICE
[0002] A portion of the disclosure of this patent document contains
material that is subject to copyright protection. The copyright
owner has no objection to the facsimile reproduction by anyone of
the patent document or the patent disclosure, as it appears in the
Patent and Trademark Office patent file or records, but otherwise
reserves all copyright rights whatsoever. Copyright 2011, Palle M.
Pedersen and Thomas C. Moran.
TECHNICAL FIELD
[0003] This invention relates to digital publishing systems,
personalized content and targeted advertisements.
BACKGROUND
[0004] The move from traditional media to digital media has enabled
the delivery of content to end users on a one-to-one basis, where
each end user receives content specifically for them, instead of
the traditional one-to-many basis (example: printing and physical
distribution of magazine, newspaper and other periodicals) where
exactly the same content is delivered to all end users.
[0005] One use of such one-to-one delivery is the delivery of
personalized content, which is content selected for particular end
users, based on criteria such as demographics, expressed interests,
and observed likes and dislikes. Personalized content spans a wide
range of applications, from Amazon's "Customers Who Bought This
Item Also Bought" feature, where customers can see products related
to the product they are currently viewing, to Mine magazine by
Time, which was a magazine in digital or print form containing
articles chosen according to each end users stated interests.
[0006] In a very similar way, targeted advertisements are
advertisements selected in an attempt to reach the most relevant
end users, based on criteria such as demographics and purchasing
behavior.
[0007] Current state-of-the-art systems for delivering targeted
advertisements fall into three groups, location-based targeting,
where the advertisement selection is done based on the current
location of a mobile device on which the advertisement is shown,
behavioral targeting, where the advertisement selection is done
based on previous user behavior (example: the original behavior of
Doubleclick), and contextual targeting, where the advertisement
selection is done based on the surrounding content (example: Google
AdSense), search terms used to find the content (example: Google
search pages) along with other context in which the advertisement
appears.
BRIEF SUMMARY OF THE INVENTION
[0008] A targeted publishing system based on profile mashups
improves on traditional methods for identifying and delivering
targeted content to specific end users by using data from sources
related to one or more addresses (including physical addresses) for
the end user in order to understand more clearly what targeted
content to deliver to such specific end user.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] FIG. 1 is a system diagram of an example of an
implementation of a targeted publishing system based on profile
mashups.
[0010] FIG. 2 is a flowchart illustrating an example of an
implementation compiling a profile mashup for an end user.
[0011] FIG. 3 is a flowchart illustrating an example of an
implementation creating a targeted content placement for a piece of
targeted content.
[0012] FIG. 4 is a flowchart illustrating an example of an
implementation creating assembled content for delivery to a
specific end user.
[0013] FIG. 5-1, FIG. 5-2, FIG. 5-3, FIG. 5-4, and FIG. 5-5 are
tables of examples of implementations for combinations of the
assembly and analysis of end user data centered around a physical
end user address for the purposes of ad targeting.
DETAILED DESCRIPTION OF THE INVENTION, INCLUDING THE PREFERRED
EMBODIMENT
[0014] In the following detailed description of the invention,
reference is made to the accompanying drawings which form a part
hereof, and in which are shown, by way of illustration, specific
embodiments in which the invention may be practiced. It is to be
understood that other embodiments may be used, and structural
changes may be made without departing from the scope of the present
invention.
INTRODUCTION
[0015] Personalized content and targeted advertisements are very
similar in the way that they attempt to match up the content (incl.
advertisements) based on the relevance of the content to the end
user, and for the purposes of this document, both personalized
content and targeted advertisements will be referred to as targeted
content.
[0016] The traditional approaches to the delivery of targeted
content are based on a notion that information about the receivers
and end users of digital content should be derived from observable
actions (e.g. their actions and selections), from user supplied
information (e.g. web-site or survey-based profiles), or from
combinations of the two. Such end user information will be referred
to as collected profiles.
[0017] However, there are many situations where either the actual
end user's identity with name and address or simply the address of
the end user is known. Typically such situations are where the end
user has a shipping address or billing address on file with a
content publisher. These situations include shopping sites and mail
order merchants used by the end user as well as publications (e.g.
news-papers, magazines, trade journals, catalogues) subscribed to
by the end user.
Operation
[0018] A targeted publishing system based on profile mashups
improves on traditional methods for identifying and delivering
targeted content to specific end users by using data from sources
related to one or more addresses (including physical addresses) for
the end user in order to understand more clearly what targeted
content to deliver to such specific end user.
[0019] When content is delivered to an end user from a targeted
publishing system based on profile mashups, each end user is
identified using a unique end user identifier (e.g. a publication
subscriber ID, email address, web-site login, account number,
security certificate, assigned UUID, unique cookie, mobile phone
number, or other unique identifier). Using this unique end user
identifier, the end user is associated with one or more end user
addresses, which are postal addresses, physical addresses, or other
means of identifying the location of a building. An end user
address can be determined in a variety of ways. If the end user is
a subscriber to a printed publication (e.g. a printed news-paper,
magazine, trade journal, or catalogue) then an end user address can
be a physical delivery address of this publication as well as a
billing address for the subscriber's account on file. If the end
user is a subscriber to an electronic publication (e.g. an online
news-paper, magazine, blog, web-site, trade journal, or catalogue)
then an end user address can be an address associated with the
subscriber profile or a billing address for the subscribers account
on file. If the relationship with the end user has been established
by a purchase of products or services, an end user address can be
the shipping address or the billing address used for the purchase.
Other ways to establish an end user address include matching up the
end user information with external databases (e.g. phone books,
employer addresses, or databases with email addresses and physical
addresses).
[0020] The end user addresses are used to find further information
from a variety of other end user data sources where an end user
address can be used to find data related to the end users. These
other end user data sources can include public and private
information for the specific end user address (e.g. property
assessment records, excise or other tax records, RMV records,
driving records, permits, credit agency data, customer lists,
warranty lists, travel history, airline mileage program history,
loyalty club records, employment information, court records, and
purchase histories) as well as aggregated information for the area
(e.g. school district information, local area news, police/fire
logs, demographics, and census data).
[0021] A profile mashup for an end user is created by first
selecting data from relevant other end user data sources based on
the end user addresses for the end user and optionally adding data
from a collected profile. The selection of data from relevant other
end user data sources can be based on an end user address in a
variety of ways, used alone or in combination, including an
approximate match on names, an approximate match on addresses, an
approximately match on normalized addresses, an approximate match
on the geo-location of addresses. In addition, the selection of
data from relevant other end user data source can be based an end
user address due to second order or higher order relationships,
where data in one relevant end user data source (examples: phone
number, identity of spouse, parents, kids, registration numbers)
can used to find data in another end user data source. This data is
then organized and processed into a set of profile mashup criteria
in order to create the final profile mashup. The profile mashup
criteria are the criteria used to select the targeted content for
the related end user and can be as simple as the data directly
present in the other end user data sources (e.g. profile mashup
criteria: "Brand of car" with values "BMW", "Mercedes", "Ford",
etc.) or a more complex derivation of the data (e.g. profile mashup
criteria: "Potential of buying high end car for young person" with
values ranging from "100%" to "0%" based on number of kids at
address between 16 and 18 years old and the purchase price of
current cars, etc.). The profile mashup criteria in use may contain
the formulas for computing the values and may be specified
explicitly or may be derived from manual or automatic search or
data-mining of the data available.
[0022] Targeted content and the related information is managed by a
targeted content management user, who is the person (or persons) in
charge specifying the targeted content as well as where the
targeted content is going to appear. A targeted content management
user can analyze the potentially targeted end users using profile
mashup examination queries, which are normal query, search, and
data-mining techniques used to examine the profile mashups. The
examination techniques include examining existing profile mashup
criteria (e.g. "Brand of car" is "BMW", "Mercedes", or "Audi"),
examining any values in the profile mashups (e.g. "BMW" is in any
profile mashup criteria), process these results using conditional,
computational or text processing formulas (e.g. "Property assessed
value"/$1MM+"Car purchase price"/$100 k), refinements and recursive
application of formulas, etc.
[0023] When a targeted content management user has found a profile
mashup examination query with a result set that can be used to
identify an appropriate set of end users, the profile mashup
examination query can either be used directly in the creation of
targeted content placements, which specify the profile mashup
examination query that is used to determine which end users to
deliver the corresponding targeted content to, or be used to create
a new profile mashup criteria with all the resulting data filled in
appropriately.
[0024] Each piece of targeted content is associated with one or
more targeted content placements. Additionally, the targeted
content placements specify a target location, which specifies the
associated location in the assembled content as well as a target
priority which is used to determine which targeted content is to be
delivered to an end user when multiple pieces of targeted content
fit a particular end user. In the case where the data from all
profile mashups are available at the time of the targeted content
placement creation, it is possible to determine the exact number of
end users to whom the targeted content will be made available, and
this number can be used during the targeting decision process.
[0025] The target location can be specified in a variety of ways,
from being a very specific location (e.g. 1/2 page advertisement on
page 4 in the final document, video introduction before content
labeled "Editorial Comment", or replacement of the section tagged
with "customized story #1") to being more unspecific (e.g. any 1/4
page advertisement or right after any article which includes the
term "luxury car"). When appropriate, the target location can use
mechanisms and formats already in use for specifying content and
location of content in the publishing systems.
[0026] The general content contains the content intended for
delivery to all end users as well as targeted content locations
with information about where targeted content fits in. The targeted
content locations can be indicated in a variety of methods, ranging
from tags and meta data embedded in the general content to the
inclusion of sample or default pieces of content which is to be
replaced with targeted content. In either case, the general content
is managed by a general content management user who in many cases
would do this using a traditional content management system (e.g.
Documentum, Adobe Dreamweaver, Adobe In Design, Quark Xpress,
etc.).
[0027] Assembled content, which is the final content delivered to
end users, is created by combining the general content with the
targeted content, using the targeted content placements. The
targeted content is selected for each end user based on the
targeted content placements, including the target priority. In
addition, other criteria traditionally used in targeted
advertisements or personalized content can be added to the
selection criteria.
[0028] The creation and delivery of the assembled content to the
end user can be done in a variety of ways and at various points of
time, often using existing infrastructure. Assembled content can be
created where it fits within the publishing process, including when
an end user requests the assembled content, on a specific schedule,
when the publisher decides to publish the content to the end user,
etc. Assembled content can be delivery to end users in a variety of
ways, including via computer networks (examples: TCP/IP network,
LAN, WAN, broadband, wireless, satellite) and through web-sites,
apps from app-stores (examples: iTunes AppStore, Android Market),
book, document and magazine readers (examples: Adobe Reader, iBook,
iPad, Kindle, Nook, Zinio, Zmags), multi-media outlets (examples:
iTunes, YouTube), etc. Assembled content can also be delivery to a
variety of devices, including hand-held devices (examples:
smart-phone, Kindle, Nook, iPad), multi-media players (examples:
MP3 player, video player), and general purpose computing devices
(examples: laptop, desktop, netbook), etc.
[0029] Note that one of the benefits of this approach is that a
specific physical device (e.g. user terminal, set top box, hand
held device, etc.) or a specific network operator (e.g. cell phone
carrier, phone company, cable operator, data service provider,
etc.) is not needed to identify the end user and the end user
address. In other words, content can be targeted without the
cooperation of the network operator or the device manufacturer,
device merchant, and/or device service provider.
Example System
[0030] FIG. 1 illustrates an example of an implementation of a
targeted publishing system based on profile mashups. In this
implementation, the general content management subsystem (104) is
used by the general content management user (102) to create, edit,
and delete general content in the general content database (106) as
well as to deliver reports on how the general content has been
used.
[0031] The targeted content management subsystem (110) is used by
the targeted content management user (108) to create, edit, and
delete targeted content in the targeted content database (112) as
well as to deliver reports on how the targeted content has been
used. The targeted content management subsystem is also used in
conjunction with the targeted content placement subsystem (114) to
create, edit and delete related targeted content placements stored
in the targeted content placement database (116).
[0032] The end user profile mashup subsystem (118) compiles the
profile mashups of end users by combining the end user information
from the end user profile database (120) with data from other end
user data sources, e.g. Property Tax Repositories (122), Credit
Agencies (124), and Other Repositories (126). The end user profile
database is typically compiled from databases of end users used by
the operators of the targeting system (examples: magazine
subscriber lists, customer lists with shipping and/or billing
addresses, etc.). The end user profile mashup subsystem (118) may
save some or all this other end user data in the end user profile
database (120), but independent of what it saves, it delivers the
profile mashup for a specific end user when requested by the
content targeting subsystem (128).
[0033] The content targeting subsystem (128) receives requests from
specific identifiable end users (130) for content, and uses the
related general content from the general content database (106)
with the information from the profile mashup for the specific end
user delivered by the end user profile mashup subsystem (120) in
order to access the targeted content placement subsystem (114)
which identifies the portions of the targeted content in the
targeted content database (112) to retrieve and assemble into
assembled content. The assembled content is then delivered by the
content targeting subsystem (128) to the end user and the end
user's specific device.
Example of Compiling a Profile Mashup
[0034] FIG. 2 shows an example of an implementation compiling a
profile mashup for an end user. In this example, end user
information is first retrieved (Step 202) using a unique end user
identifier, after which the list of end user addresses associated
with this end user is extracted (Step 204). The end user
information could be as simple as an end user address or it could
contain a more complex set of information to supplement the end
user address.
[0035] For each end user address associated with this end user, the
end user address is normalized (Steps 206, 208, 210) into a form
which makes it easier to look up data in the other end user data
sources. The other end user data sources are now examined one by
one (Steps 212, 214, 216) by doing a look up using the normalized
end user address (Step 218). If any data that relates to the end
user is found in the data source (Step 220), this data is added to
the end user profile mashup (Step 222).
[0036] When all the other end user data sources have been examined
using all the end user addresses, the collected profile data is
added (Step 224), all data is processed according to the end user
profile criteria (Step 226), and the final end user profile mashup
is stored along with the unique end user identifier (Step 228) for
further reference.
[0037] In the implementation of FIG. 1, the content targeting
subsystem (128) retrieves end user information (Step 202) using a
unique end user identifier provided by the end user or end user's
device (130), after which the list of end user addresses associated
with this end user is extracted (Step 204) from the end user
profile database (120), which may in turn have been derived from
publication subscriber lists, customer lists or other lists with
shipping, billing or other addresses, etc. The end user information
could be as simple as an end user address or it could contain a
more complex set of information to supplement the end user
address.
[0038] For each end user address associated with this end user, the
end user address is normalized (Steps 206, 208, 210) into a form
which makes it easier to look up data in the other end user data
sources (122, 124, 126). The end user profile mashup subsystem
(118) now examines the other end user data sources one by one
(Steps 212, 214, 216) by doing a look up using the normalized end
user address (Step 218). If any data that relates to the end user
is found in the data source (Step 220), the end user profile mashup
subsystem data adds this data to the end user profile mashup (Step
222).
[0039] When the end user profile mashup subsystem (118) have
examined all the other end user data sources (122, 124, 126) using
all the end user addresses from the end user profile database
(120), the subsystem adds the collected profile data (Step 224)
from the end user profile database (120), and process all the data
according to the end user profile criteria (Step 226), also found
in the end user profile database (120), and the final end user
profile mashup is stored in the end user profile database (120)
along with the unique end user identifier (Step 228) for further
reference. Other hardware implementations are possible.
Example of Targeting Content
[0040] FIG. 3 shows an example of an implementation creating a
targeted content placement for a piece of targeted content. In this
example, a profile mashup examination query, a target location, and
a target priority for a piece of targeted content are supplied by a
targeted content management user. If there is a request for saving
the profile mashup examination query as a profile mashup criteria
(Step 302) and if no profile mashup criteria for the profile mashup
examination query is found (Step 304), a new profile mashup
criteria which includes the profile mashup examination query is
added (Step 306). The values for these new profile mashup criteria
are then filled in for all the profile mashups (Step 308), and a
new profile mashup examination query using these profile mashup
criteria will be used instead of the original profile mashup
examination query in the following processing (Step 310).
[0041] Using the profile mashup examination query which was either
found or newly created along with the target location and target
priority, a targeted content placement is created (Step 312). This
targeted content placement and its association with the targeted
content are then added to system (Step 314).
[0042] In the implementation of FIG. 1, a profile mashup
examination query, a target location, and a target priority for a
piece of targeted content are supplied by a targeted content
management user (108) to the targeted content management placement
subsystem (114) through the target management subsystem (110). If
the targeted content management user request to save the profile
mashup examination query as a profile mashup criteria (Step 302),
then the end user profile mashup subsystem (118) checks if a
profile mashup criteria for the profile mashup examination query
already exists in the end user profile database (120) (Step 304),
and if it does not exist, a new profile mashup criteria which
includes the profile mashup examination query is added to the end
user profile database (Step 306). The values for these new profile
mashup criteria are then filled in for all the profile mashups
(Step 308) and the values stored in the end user profile database
(120) by the end user profile mashup subsystem (118), and a new
profile mashup examination query using these profile mashup
criteria will be used instead of the original profile mashup
examination query in the following processing (Step 310).
[0043] Using the profile mashup examination query which was either
found or newly created along with the target location and target
priority, a targeted content placement is created (Step 312) by the
targeted content placement subsystem (114). This targeted content
placement and its association with the targeted content are then
added to the targeted content placement database (116) (Step
314).
[0044] Other hardware implementations are possible.
Example of Assembling Content
[0045] FIG. 4 shows an example of an implementation creating
assembled content for delivery to a specific end user. In this
example, the general content is first retrieved (Step 402) after
which all targeted content locations in the general content are
found (Step 404). All entries in this list of targeted content
locations are initially unmarked, and thus available. This general
content is also used as the starting point for the assembled
content (Step 406), which is used to combine the targeted content
with the general content.
[0046] The end user profile mashup is then retrieved for the end
user whom the assembled content is being assembled for (Step 408).
This retrieval could, by example, be done as illustrated in FIG. 2.
Using this end user profile mashup and the list of targeted content
locations, all potential targeted content placements for this end
user and general content combination are found (Step 410) and the
list of targeted content placements is ordered by target priority
(Step 412).
[0047] This ordered list of targeted content placements is now
processed one by one and, while there are still targeted content
placements left to process (Step 414), check the target location
for this target against the remaining entries in the list of
targeted content locations which has not been marked not available
(Step 416).
[0048] If no corresponding targeted content location is available,
then skip this target content placement (Step 418), otherwise
insert the targeted content for this target content placement into
the assembled content as indicated by the targeted content location
(Step 420) and mark this targeted content location as not available
(Step 422), since it has been filled with targeted content for this
target content placement.
[0049] When there are no more targeted content placements to
process for the assembled content, return the assembled content to
the application or directly to the end user (Step 424).
[0050] In the implementation of FIG. 1, after one of the end users
(130) request content from the content targeting subsystem (128),
the general content is retrieved (Step 402) from the general
content database (106) by the content targeting subsystem, after
which all targeted content locations in the general content are
found (Step 404) by the targeted content placement subsystem (114)
from the targeted content placement database (116). All entries in
this list of targeted content locations are initially unmarked, and
thus available. This general content is also used by the content
targeting subsystem (128) as the starting point for the assembled
content (Step 406), which is used to combine the targeted content
stored in the targeted content database (112) with the general
content stored in the general content database (106).
[0051] Using a unique end user identifier supplied by the end user
(130) in connection with the request for content from the content
targeting subsystem (128), the content targeting subsystem
retrieves the end user profile mashup from the end user profile
mashup subsystem (118) (Step 408). This retrieval could, by
example, be done as illustrated in FIG. 2. The content targeting
subsystem (128) use this end user profile mashup retrieved from the
end user profile mashup subsystem (118) and the list of targeted
content locations retrieved from the targeted content placement
subsystem (114) to find all potential targeted content placements
for this end user and general content combination (Step 410) and
the list of potential targeted content placements is ordered by the
target priority (Step 412).
[0052] This ordered list of targeted content placements is now
processed one by one by the content targeting subsystem (128), and
while there are still targeted content placements left to process
(Step 414) check the target location for this target against the
remaining entries in the list of targeted content locations which
has not been marked not available (Step 416).
[0053] If no corresponding targeted content location is available,
then the content targeting subsystem (128) skip this target content
placement (Step 418), otherwise content targeting subsystem insert
the targeted content from targeted content database (112) for this
target content placement into the assembled content as indicated by
the targeted content location (Step 420) and mark this targeted
content location as not available (Step 422), since it has been
filled with targeted content for this target content placement.
[0054] When there are no more targeted content placements to
process for the assembled content, the content targeting subsystem
(128) return the assembled content to the end user (130) who
originally requested the content (Step 424).
[0055] Other hardware implementations are possible.
Examples of Targeting Using Specific Data
[0056] FIG. 5-1, FIG. 5-2, FIG. 5-3, FIG. 5-4, and FIG. 5-5 show
tables of examples of implementations for combinations of the
assembly and analysis of end user data centered around an address
for the purposes of ad targeting. The first column, Origin of end
users, describes examples of the means by which an end user becomes
part of the ad targeting process. These can include using a mobile
device, utilizing a digital service within a checkout kiosk or
perhaps buying or subscribing to a digital product such as a
magazine or periodical. The second column, End user profile source,
describes examples of the initial source providing the end user
profile information. These can include a publisher's list of
subscribers, an ecommerce website or other website providing
profiles of their registered end users, a merchant's customer list
of those end users participating in royalty or rewards programs,
The third column, Unique end user identifier, describes examples of
the unique identifier assigned to each end user as part of the end
user profile provided in the second column. The fourth column, End
user address, describes examples of a physical address or in some
cases multiple addresses associated with each end user. These
addresses can be mailing, billing, home, business or other physical
address. The fifth column, Additional data sources, describes
examples of data acquired from additional other end user data
sources that can be mashed up and assembled with the physical
address of the end user as well as with the profile information to
enable specific content targeting. The sixth column, Targeting,
describes examples of specifically targeted content that an end
user may receive based on the mashup and analysis of the compiled
end user profile mashup.
Other Embodiments
[0057] As will be apparent to those skilled in the art, many
aspects of the disclosure may be implemented in various differing
fashion while still functioning with a targeted publishing system
based on profile mashups.
[0058] Although this implementation is described with certain part
of the processing done by the content targeting subsystem and other
parts by the end user profile mashup subsystem, the targeted
content placement subsystem, the targeted content management
subsystem, and the general content management subsystem,
alternative implementations of the system could be implemented
where parts of the processing is performed by another subsystem
than the subsystem mentioned in this implementation.
[0059] Another implementation could define an end user address as
something else than a physical address, which can still be used to
correlate data about the end user.
[0060] Another implementation could use end user addresses directly
without normalizing them, and instead use search or lookup
mechanisms related to each data source in order to compile the
profile mashup.
[0061] Another implementation could normalize end user addresses
into a format specific for each data source.
[0062] Another implementation could differentiate between end user
addresses which identifies the end user with varying degrees of
specificity (e.g. as an individual, an address, an apartment
complex, or an office building), and apply the data from the other
end user data sources differently to the profile mashup based on
the specificity of either the identification of the end user or the
address in the data source.
[0063] Another implementation can include refinements, priorities,
or selections of end user addresses based on address origin, and
other matching of data.
[0064] Another implementation could combine profile mashup
information with current physical location (from geo-location
information) to further refine relevant content, including using
the address of the current physical location as another end user
address.
[0065] Another implementation could combine this targeting method
with behavioral targeting, by adding, tracking, or referencing
behavioral data in the collected profile which is turn is used in
the profile mashup.
[0066] Another implementation could combine this targeting method
with contextual targeting, by additionally using contextual data
when creating targeted content locations and targeted content
placements.
[0067] Another implementation could store profile mashups as a
combination of structured and unstructured data from the other end
user data sources, which could be fully or partially created
on-the-fly, when a profile mashup for an individual end user is
requested.
[0068] Another implementation could create the profile mashup
criteria fully or partially created on-the-fly, when a profile
mashup for an individual end user is requested.
[0069] Another implementation could manage the targeted content
within the same database as the general content while only managing
the targeted content placements separately.
[0070] Another implementation could store the targeted content
locations separately from the general content.
[0071] Another implementation could have general content which only
contains targeted content locations and as a result publish only
targeted content to end users.
[0072] Another implementation could identify targeted content
locations based on how general content management systems stores
content and identifies sections of the content.
[0073] Another implementation could extend the general content
management subsystem to add meta information and targeting
information to the general content in order to help the content
targeting subsystem identify or replace the relevant content.
[0074] Another implementation could implement pre-compute the
values of profile mashup examination queries used in targeted
content placements and save them in profile mashup criteria in
order to have the relevant list of targeted content placements
readily available when assembling content for a specific end
user.
[0075] Another implementation could extend the targeted content
placements and the targeting mechanism to additionally include
traditional methods used in creating targeted advertisements and
personalized content.
[0076] Another implementation could determine the priorities of
potential targeted content placements based on additional
information from the general content, targeted content, end user
profile mashup, or a combination of the above.
[0077] Another implementation could split the tasks of the targeted
content management user between managing personalized content and
managing targeted advertisements.
[0078] Another implementation could look up multiple end users in a
data source at a time instead of looking them up one by one.
[0079] Another implementation could use parallel processing
techniques to process the ordered list of targeted content
placements in parallel instead of processing them one by one.
[0080] Another implementation could run the general content
management subsystem or the complete system in-house in an
organization instead of over a network.
[0081] Another implementation could integrate into an existing
publishing pipeline using an existing general content management
subsystem and/or the existing mechanism for delivering the
assembled content to the end user.
[0082] Another implementation could deliver the assembled content
to an individual on a physical medium (magazine, news-paper, CD,
DVD, Blu-ray disc, etc.).
[0083] Another implementation could create the assembled content to
end users and deliver it them on a schedule or when the general
content or targeted content is ready for publishing.
[0084] Another implementation could run multiple instances of the
system in a multi-tenant configuration while segregating or
partially sharing the end user profile mashups across these
instances.
[0085] Another implementation could implement the examination of
profile mashups using standard search engine techniques or data
mining techniques or both.
[0086] Another implementation could add traditional on-line
advertisement placement techniques to the strategies used in
assembling content.
[0087] Another implementation could add customization of assembled
content based on the end user device--including a dedicated reading
device, a multi-media player (a MP3 player, a video player, etc.),
a hand held device (a smart phone, an iPad, etc.), and a general
purpose computer (a laptop, a desktop, etc.).
[0088] Another implementation could assemble the content
dynamically on the fly when individual portions of the content are
requested by the end user.
[0089] Another implementation could assemble the content on the end
user device, where the general content, targeted content, and end
user profile data could come from the same location or different
locations.
[0090] Another implementation could implement the general content
management system on the Internet and reference the targeted
content through links (URLs, URIs, etc) in the general content.
[0091] Another implementation could automate the functions of the
targeted content management user with an automated system utilizing
business, artificial and other automated intelligence techniques to
make determinations about targeted content.
[0092] Another implementation could bypass the need for the end
user to make a request to the content targeting subsystem by
pre-assembling the end user's selected targeted content based on
the targeted content placements and store the pre-assembled content
until the end user makes the request for download.
[0093] Another implementation could define target priority as a
value determined by meta data analysis, A.I. and information from
other end user data sources that ascribe a value to certain end
users as it related to the targeted content and targeted content
placements.
* * * * *